{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot  as plt\n",
    "from sklearn.metrics import accuracy_score\n",
    "import numpy as np\n",
    "import seaborn as sns; sns.set()\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "构造数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets.samples_generator import make_blobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x69a67b8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y_true = make_blobs(n_samples=300, centers=4, cluster_std=0.60, random_state=0)\n",
    "plt.scatter(X[:,0], X[:, 1], s=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "kmeans = KMeans(n_clusters=4)\n",
    "kmeans.fit(X)\n",
    "y_kmeans = kmeans.predict(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "绘制聚类结果，  画出聚类中心"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x5443320>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:, 0], X[:, 1], c=y_kmeans, s=50, cmap='viridis')\n",
    "\n",
    "centers = kmeans.cluster_centers_\n",
    "plt.scatter(centers[:,0], centers[:, 1], c='black', s=80, marker='x')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "核K-means算法，应用于边界非线性的情况"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "构造数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_moons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = make_moons(200, noise=0.05, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "传统kmeans聚类失败的情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x549c908>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = KMeans(n_clusters=2, random_state=0).fit_predict(X)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=labels, s=50, cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "应用核方法， 将数据投影到更高纬的空间，\n",
    "\n",
    "变成线性可分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import SpectralClustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TG\\Anaconda3\\lib\\site-packages\\sklearn\\manifold\\spectral_embedding_.py:234: UserWarning: Graph is not fully connected, spectral embedding may not work as expected.\n",
      "  warnings.warn(\"Graph is not fully connected, spectral embedding\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xbef7588>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = SpectralClustering(n_clusters=2, affinity='nearest_neighbors', assign_labels='kmeans')\n",
    "labels = model.fit_predict(X)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=labels, s=50, cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# k-means算法处理手写数字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 64)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits = load_digits()\n",
    "digits.data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "进行聚类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 64)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=10, random_state=0)\n",
    "clusters = kmeans.fit_predict(digits.data)\n",
    "kmeans.cluster_centers_.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以将这些族中心点看做是具有代表性的数字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x216 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 5, figsize=(8, 3))\n",
    "centers = kmeans.cluster_centers_.reshape(10, 8, 8)\n",
    "for axi, center in zip(ax.flat, centers):\n",
    "    axi.set(xticks=[], yticks=[])\n",
    "    axi.imshow(center, interpolation='nearest', cmap=plt.cm.binary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将每个学习到的标签与族标签想匹配\n",
    "\n",
    "众数匹配"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import mode\n",
    "\n",
    "labels = np.zeros_like(clusters)\n",
    "for i in range(10):\n",
    "    #得到聚类结果第i类的 True Flase 类型的index矩阵\n",
    "    mask = (clusters ==i)\n",
    "    #根据index矩阵，找出这些target中的众数，作为真实的label\n",
    "    labels[mask] = mode(digits.target[mask])[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有了真是的指标，可以进行准确度计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7935447968836951"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(digits.target, labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看混淆矩阵（可以看出，哪个预测出错了）\n",
    "\n",
    "主要是出现在8  1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "mat = confusion_matrix(digits.target, labels)\n",
    "np.fill_diagonal(mat, 0)\n",
    "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=False,\n",
    "            xticklabels=digits.target_names,\n",
    "            yticklabels=digits.target_names)\n",
    "plt.xlabel('true label')\n",
    "plt.ylabel('predicted label');"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
